Modulhandbuch ab WS 2020/21

Modul CS4575-KP04

Sequence Learning (SEQL)


1 Semester

Jedes Sommersemester

Studiengang, Fachgebiet und Fachsemester:
  • Master Medizinische Ingenieurwissenschaft 2020 (Wahlpflicht), Wahlpflicht, Beliebiges Fachsemester
  • Master Entrepreneurship in digitalen Technologien 2020 (Wahlpflicht), Wahlpflicht, Beliebiges Fachsemester
  • Master Medieninformatik 2020 (Wahlpflicht), Wahlpflicht, Beliebiges Fachsemester
  • Master Biophysik 2023 (Wahlpflicht), Wahlpflicht, Beliebiges Fachsemester
  • Master Psychologie 2016 (Wahlpflicht), Wahlpflicht, Beliebiges Fachsemester
  • CS4575-V: Sequence Learning (Vorlesung, 2 SWS)
  • CS4575-Ü: Sequence Learning (Übung, 1 SWS)
  • 75 Stunden Selbststudium
  • 45 Stunden Präsenzstudium
  • Introduction to Sequence Learning (Formalisms, Metrics, Recapitulation of Relevant Machine Learning Techniques)
  • Recurrent Neural Networks (Simple RNN Models, Backpropagation Through Time)
  • Gated Recurrent Networks (Vanishing Gradient Problem in RNNs, Long Short-Term Memories, Gated Recurrent Units, Stacked RNNs)
  • Important Techniques for RNNs (Teacher Forcing, Scheduled Sampling, h-Detach)
  • Bidirectional RNNs and related concepts
  • Hierarchical RNNs and Learning on Multiple Time Scales
  • Online Learning and Learning without BPTT (Real-Time Recurrent Learning, e-Prop, Forward Propagation Through Time)
  • Reservoir Computing (Echo State Networks, Deep ESNs)
  • Spiking Neural Networks (Spiking Neuron Models, Learning in SNNs, Neuromorphic Computing, Recurrent SNNs)
  • Temporal Convolution Networks (Causal Convolution, Temporal Dilation, TCN-ResNets)
  • Introduction to Transformers (Sequence-to-Sequence Learning, Basics on Attention, Self-Attention and the Query-Key-Value Principle, Large Language Models)
  • State Space Models (Structured State Space Sequence Models, Mamba)
  • Students get a comprehensive understanding of most relevant sequence learning approaches
  • Students learn to analyze the challenges in sequence learning tasks and to identify well-suited approaches to solve them
  • Students will understand the pros and cons of various sequence learning models
  • Students can implement common and custom sequence learning models for time series analysis, classification, and forecasting
  • Students know how to analyze the models and results, to improve the model parameters, and to interpret the model predictions and their relevance
Vergabe von Leistungspunkten und Benotung durch:
  • Klausur oder mündliche Prüfung nach Maßgabe des Dozenten
  • Prof. Dr. Sebastian Otte
  • MitarbeiterInnen des Instituts
  • Prof. Dr. Sebastian Otte
  • Goodfellow, I., Bengio, Y., & Courville, A. (2016): Deep Learning - MIT Press. ISBN 978-0262035613
  • Prince, S. J. D. (2023): Understanding Deep Learning - The MIT Press. ISBN 978-0262048644
  • Deisenroth, M. P., Faisal, A. A., & Ong, C. S. (2020): Mathematics for Machine Learning - Cambridge University Press, 2020. ISBN 978-1108470049
  • Nakajima, K., & Fischer, I. (2021): Reservoir Computing: Theory, Physical Implementations, and Applications - Cambridge University Press, 2020. ISBN 978-1108470049
  • Sun, R., & Giles, C. (2001): Sequence Learning: Paradigms, Algorithms, and Applications - Springer Berlin Heidelberg. ISBN 978-3540415978
  • Bishop, C. M. (2006): Pattern Recognition and Machine Learning - Springer. ISBN 978-0387310732
  • Recent publications on the related topics:
  • Wird nur auf Englisch angeboten

Admission requirements for taking the module:
- None, but it is recommended to complete the course Deep Learning (CS4295-KP04) first

Admission requirements for participation in module examination(s):
- Successful completion of exercise assignments as specified at the beginning of the semester

Module Exam(s):
- CS4575-L1: Sequence Learning, exam, 90 min

Letzte Änderung:

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